Depression Risk Prediction among Tech Employees Using Machine Learning

Depression is a growing concern in the tech industry due to high workload, tight deadlines, and constant exposure to digital environments.This study explores the application of machine learning algorithms to predict the risk of depression among tech employees based on workplace, behavioural, and psychological factors.Data was collected through surveys incorporating standardized mental health assessment scales and workplace-related parameters.Various supervised learning models such as Random Forest, Support Vector Machine, and Logistic Regression were trained and evaluated.The Random Forest model achieved the highest accuracy in identifying individuals at risk.Feature importance analysis revealed key predictors including work-life balance, sleep, patterns, and job satisfaction.The models predictions can help HR and mental health professionals implement timely interventions.This research demonstrates the potential of machine learning in supporting mental health awareness and early risk detection in corporate tech environments.Furthermore, organizations are increasingly employing predictive analytics using machine learning models that analyse HR records, workplace behaviour, and self-reported surveys.These tools help in identifying employees at risk and allow for timely intervention.As a result, companies are better equipped to formulate evidence-based wellness strategies tailored to their workforce.Workplace policy reform is another significant application.Many tech companies are now adopting mental health-friendly policies such as flexible working hours, provision of mental health days, and resources for counselling and psychological support.Additionally, employee training programs on mental health literacy for managers and peer support systems foster a more supportive and stigma-free work environment.